Our model generates molecules by attaching a chemically realistic and pharmacochemically acceptable fragment unit on a given state of molecules at each stepIn this work, we widely define the term “chemically realistic molecule" as a stable molecule and narrowly define it as a molecule that is an assembly of fragments that appear in the ZINC data. Also, we widely define the term “inappropriate molecule/fragment" or “pharmacochemically inacceptable molecule/fragment" as a molecule that has nonspecific toxicity or reactivity, and narrowly define it as a molecule that cannot pass through the three medicinal chemistry filters, which are Glaxo, PAINS, SureChEMBL filters.. We enforce the model to form a new bond only on the attachment sites that are considered as appropriate at the fragment library preparation step.
These strategies enable us to utilize medicinal chemistry prior knowledge and successfully constrain the molecule generation within the chemical space eligible for drug design.
We also explore several explorative algorithms based on curiosity-driven learning and prioritized experience replay (PER) [1]}.
We devise an effective novel PER method that defines priority as the novelty of the experiences estimated by the predictive error or uncertainty of the auxiliary reward predictor's outcome. With this method, we aim to avoid the lack of robustness of previous methods and encourage the exploration of diverse solutions. We provide an overall illustration of our framework in Figure REF .
